help heabs

Title

heabs Calculates the ICER and Net Benefit for up to two datasets, and produces an assessment of the agreement of the datasets.
Requires paired individual level cost and effect data from a two arm trial or equivalent.

Syntax

heabs cost1 effect1 [cost2 effect2],
intervention(varname)
response(str)
[w2p(real)]

Description

Returns a range of estimates including ICER, Net Benefit. If two datasets are presented, Lin's CCC is also calculated.
Designed to be used for bootstrapping, to allow the calculation of the probability of miscoverage and probability of cost effectiveness.

If response is bene (meaning a higher score is beneficial to the individual), ICER is calculated as:

(meanCost[intervention=1] - meanCost[intervention=0]) / (meanEffect[intervention=1] - meanEffect[intervention=0])

If response is detr (meaning a higher score is detrimental), ICER is calculated as:

(meanCost[intervention=1] - meanCost[intervention=0]) / (meanEffect[intervention=0] - meanEffect[intervention=1])

Net benefit is calculated following the same relative logic.

Options

intervention(var) used to indicate which treatment group patients are allocated to. Must be encoded 0 and 1.

response(str) specifies whether a higher effect score is positive (bene) or negative (detr)

w2p(#) the willing-to-pay level to be examined, must be real and positive, default 0.

Stored Results

SCALARS

r(NB1) Incremental Net Benefit calculated from the first dataset.

r(seNB1) Standard Error of the Incremental Net Benefit from the first dataset.

r(loCINB1) Lower 95% Confidence Interval of the Incremental Net Benefit from the first dataset.

r(upCINB1) Upper 95% Confidence Interval of the Incremental Net Benefit from the first dataset.

r(ICER1) Incremental Cost Effectiveness Ratio from the first dataset.

r(NB2) Incremental Net Benefit calculated from the second dataset.

r(seNB2) Standard Error of the Incremental Net Benefit from the second dataset.

r(loCINB2) Lower 95% Confidence Interval of the Incremental Net Benefit from the second dataset.

r(upCINB2) Upper 95% Confidence Interval of the Incremental Net Benefit from the second dataset.

r(ICER2) Incremental Cost Effectiveness Ratio from the second dataset.

r(diffNB) Difference in Incremental Net Benefit Scores between the two datasets. (NB2 - NB1)

r(cccNB) Lin's Concordance Correlation Coefficient for the Incremental Net Benefit of the two datasets.

r(zcccNB) Z Score of the Concordance Correlation Coefficient.

Examples

heabs cost1 effect1, res(bene) int(treatID) d w2p(5000)

heabs cost1 effect1 cost2 effect2, res(detr) int(treatID) w2p(5000)

bootstrap DiffNetben=r(diffNB) cost1=r(cost1) cost2=r(cost2) effect1=r(outcome1) effect2=r(outcome2) NB1=r(NB1) NB2=r(NB2) ICER1=r(ICER1) ICER2=r(ICER2) ///
NB1Lo=r(loCINB1) NB1Up=r(upCINB1) NB2Lo=r(loCINB2) NB2Up=r(upCINB2) ///
, saving(BSoutput, replace) reps(1000) seed(24): ///
heabs cost1 effect1 cost2 effect2, w2p(0) intervention(allocation) response(detr)

Authors

Daniel Gallacher

Warwick Clinical Trials Unit

University of Warwick

D.Gallacher@Warwick.ac.uk